CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data

Author summary In parallel with developments in genomic technologies over the last decades, ancient genomics opened a new era in understanding the evolutionary history of populations and species. However, the field still needs novel computational methods for accurate and effective use of ancient genome data, which is mostly low-coverage and more challenging to analyse than modern-day genomes. Single nucleotide polymorphisms (SNPs), to date, have yet been the main source of information analysed in ancient genome studies. This is despite copy number variants (CNVs) harboring at least as much information as SNPs, especially with respect to natural selection. Here we developed CONGA, an algorithm for genotyping deletions and duplications in low-coverage genomes. We assessed its accuracy using simulations (with ancient-like data), and also studied its performance among 71 real ancient human genomes from different laboratories. We found that the common practice of authors filtering their ancient genome data before publishing prevents the reliable identification of duplications. Meanwhile, large (>1,000 base-pair) deletions can be detected even at quite low coverage (e.g. 0.5×). Deletions called in ancient genomes reflect population history and also show signs of negative selection.